Required Datapoints
- Number of Referral Prompts Shown
- Number of Users Who Accepted the Prompt (clicked “Yes” or continued)
- Prompt Type and Delivery Method
Referral Prompt Acceptance Rate measures the percentage of users who respond positively when presented with a referral prompt—e.g., clicking “Yes, I’ll refer” or continuing into the referral flow. It helps assess referral intent and the effectiveness of trigger timing.
Referral Prompt Acceptance Rate is a key indicator of advocacy intent and timing alignment, reflecting how often users agree to refer after receiving a targeted referral nudge—via in-app, email, or lifecycle automation.
The relevance and interpretation of this metric shift depending on the model or product:
A high acceptance rate signals emotional readiness, great timing, and aligned messaging, while a low rate may reflect fatigue, misfires, or unclear value exchange.
By segmenting by prompt type, user segment, or channel, you can refine who gets asked, when, and with what incentive framing.
Referral Prompt Acceptance Rate informs:
These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome
Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.
Activities commonly tied to improving or operationalizing this KPI.
| Activity | Description |
|---|---|
| Referral Prompt Design | Referral Prompt Design involves strategically designing, testing, and refining prompts and calls-to-action to encourage users or customers to refer others to a product or service. It gives teams a clear plan for where to focus, how to sequence work, and what to measure. Relevant KPIs include Referral Prompt Acceptance Rate and Referral Prompt Interaction Rate. |
| Lifecycle Campaigns | Lifecycle Campaigns focuses on Lifecycle marketing orchestrates a series of targeted, automated campaigns to engage, nurture, convert, and retain customers throughout their journey. It coordinates execution across touchpoints so teams can move users or accounts toward the target outcome. Relevant KPIs include Feature Adoption Rate (Ongoing) and Referral Prompt Acceptance Rate. |
| NPS-Based Targeting | NPS-Based Targeting focuses on NPS (Net Promoter Score)-based segmentation leverages customer feedback from NPS surveys to identify and prioritize accounts or users most likely to engage or expand. It gives teams a clear plan for where to focus, how to sequence work, and what to measure. Relevant KPIs include Referral Prompt Acceptance Rate. |
| Offer Personalization | Offer Personalization is the strategic process of customizing product or service offerings to match the unique needs, preferences, or behaviors of individual customers or specific segments. It helps teams translate strategy into repeatable execution. Relevant KPIs include Referral Prompt Acceptance Rate. |
1,800 in-app referral prompts shown this month
630 users clicked “Yes” or “Continue”
Formula: 630 ÷ 1,800 = 35% Referral Prompt Acceptance Rate
This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:
This KPI is classified as a lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.
This role is directly accountable for the KPI and is expected to drive progress and decisions around it.
These roles contribute directly to performance and typically partner on execution, reporting, or optimization.
These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.
cube('ReferralPrompts', { sql: `SELECT * FROM referral_prompts`,
measures: { numberOfPromptsShown: { sql: `number_of_prompts_shown`, type: 'sum', title: 'Number of Referral Prompts Shown', description: 'Total number of referral prompts shown to users.' }, numberOfAcceptances: { sql: `number_of_acceptances`, type: 'sum', title: 'Number of Users Who Accepted the Prompt', description: 'Total number of users who accepted the referral prompt.' }, acceptanceRate: { sql: `100.0 * ${numberOfAcceptances} / NULLIF(${numberOfPromptsShown}, 0)`, type: 'number', title: 'Referral Prompt Acceptance Rate', description: 'Percentage of users who accepted the referral prompt.' } },
dimensions: { id: { sql: `id`, type: 'number', primaryKey: true, title: 'ID', description: 'Unique identifier for each referral prompt record.' }, promptType: { sql: `prompt_type`, type: 'string', title: 'Prompt Type', description: 'Type of referral prompt shown to the user.' }, deliveryMethod: { sql: `delivery_method`, type: 'string', title: 'Delivery Method', description: 'Method used to deliver the referral prompt to the user.' }, createdAt: { sql: `created_at`, type: 'time', title: 'Created At', description: 'Timestamp when the referral prompt was created.' } }});Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema